Cambridge Housing Model (BEIS)

We developed this model over three years for BEIS (previously known as DECC), to underpin the Housing Energy Fact File and Energy Consumption in the UK, and to inform housing policy decisions. It was published to encourage scrutiny of the model, and in line with Government policies on transparency. The model is is now feeding into the National Household Model – a model for Great Britain that will run upgrade scenarios. The Cambridge Housing Model uses English Housing Survey data, coupled to a SAP-based energy calculator, to estimate energy use and CO2 emissions for all homes in England, broken down by final use. The Model and User Guide are available on Hightail.

We built on this experience by developing a non-domestic energy model, which allows you to explore the effect of upgrading different numbers of buildings on energy use and carbon dioxide. Non-domestic buildings have long been neglected in energy modelling in favour of housing – partly because the diversity of non-domestic buildings makes them harder to model, and partly because there is much less reliable data available describing non-domestic buildings.

However, non-domestic buildings currently account for around a fifth of total carbon emissions. Part of these emissions come from so-called ‘regulated’ energy uses (like heating, lighting and air conditioning), while the rest comes from ‘unregulated’ energy uses (like catering, lifts, computers and other appliances).

Our model addresses both. It also includes the effect of new buildings being added to the stock, and old buildings being demolished. It suggests that – if we pulled out all the stops and worked really hard on non-domestic buildings – the UK could save 45 TWh of energy by insulating all of these buildings by 2022, or just under 25% of energy use in this sector. Or, if we both insulate all these buildings and upgrade their lighting to modern, energy efficient lights, we could save 72 TWh (38%).

The model allows you to change assumptions about the carbon intensity of electricity over time, and demolitions and new building. It also shows the regional impact of different upgrade paths, year-by-year to 2022.

A working version of the model is available for download here.